Legal claims defining the scope of protection. Each claim is shown in both the original legal language and a plain English translation.
1. A method for image identification using a secure autonomous intelligent server, the method comprising: creating a database of known logos, the database comprising vertices of geometric shapes formed from the known logos; wherein the creating the database of known logos comprises: obtaining a known logo; creating blurred variations of the known logo, the blurred variations comprising the known logo portrayed in varying levels of blur; identifying key points on the known logo and the blurred variations; constructing a geometric shape from the key points of the known logo and the blurred variations; calculating vertices of the geometric shape of the known logo and the blurred variations; and storing the vertices of the geometric shape of the known logo and the blurred variations in the database and associating the vertices with the known logo and the blurred variations; obtaining an unidentified logo representing an object in a query image; identifying key points on the unidentified logo; constructing a geometric shape from the key points of the unidentified logo; calculating vertices of the geometric shape of the unidentified logo; matching the vertices of the geometric shape of the unidentified logo with the vertices of the geometric shape of at least one of the known logos and the blurred variations; constructing multiple geometric shapes comprising a plurality of triangles from the key points of the known logo and the blurred variations; calculating vertices of each of the multiple geometric shapes of the known logo and the blurred variations; constructing multiple geometric shapes comprising a plurality of triangles from the key points of the unidentified logo; calculating vertices of each of the multiple geometric shapes of the unidentified logo; and matching the vertices of two or more of the geometric shapes comprising the plurality of triangles of the unidentified logo with the vertices of the geometric shapes comprising the plurality of triangles of one of the known logos and the blurred variations.
Image recognition and authentication. This invention addresses the challenge of identifying logos within images, particularly in a secure and automated manner. The core of the method involves creating a robust database of known logos. This database is populated by processing known logos to generate blurred variations, simulating different levels of image degradation. Key points are identified on both the original and blurred logos. Geometric shapes are then constructed from these key points, and their vertices are calculated. These vertices, along with their association to the original logo and its blurred variations, are stored in the database. When an unidentified logo is presented in a query image, key points are identified, and a geometric shape is constructed. The vertices of this unidentified logo's geometric shape are then compared against the stored vertices in the database. Furthermore, the method involves constructing multiple geometric shapes, specifically triangles, from the key points of both known and unidentified logos. The vertices of these triangular shapes are calculated and matched. This allows for a more comprehensive comparison by matching multiple triangular shapes from the unidentified logo against those of a known logo and its blurred variations. The use of a secure autonomous intelligent server implies the process is automated and protected.
2. The method of claim 1 , wherein the constructing a geometric shape from the key points of the unidentified logo comprises: identifying ordered pairs of the key points; identifying the ordered pairs that have one key point in common; and constructing the geometric shape from the ordered pairs having one key point in common.
This invention relates to logo recognition systems, specifically methods for constructing geometric shapes from key points of an unidentified logo to facilitate recognition. The problem addressed is the difficulty in accurately identifying logos when their key points are scattered or unordered, making it challenging to reconstruct the logo's structure for matching against a database. The method involves extracting key points from an image of an unidentified logo, then constructing a geometric shape by identifying ordered pairs of these key points. The process includes determining which ordered pairs share a common key point, and using these overlapping pairs to form the geometric shape. This approach ensures that the reconstructed shape accurately represents the logo's structure, improving recognition accuracy. The geometric shape construction step is critical because it organizes the key points into a coherent structure that can be compared against known logo templates. By focusing on ordered pairs with shared key points, the method avoids errors caused by misaligned or incorrectly ordered points, leading to more reliable logo identification. This technique is particularly useful in applications where logos may appear distorted or partially obscured, such as in surveillance footage or low-resolution images. The method enhances the robustness of logo recognition systems by ensuring that the geometric representation of the logo is consistent and comparable to stored templates.
3. The method of claim 1 , wherein the data base of vertices of geometric shapes formed from the known logos comprises vertices of triangles.
A system and method for analyzing and recognizing logos in digital images involves a database of geometric shapes derived from known logos, where the shapes are represented by their vertices. The method extracts geometric features from an input image, such as edges or contours, and compares these features to the stored shapes in the database. The comparison process identifies matches between the extracted features and the database entries, enabling logo recognition. The database includes vertices of triangles, which are used to define the geometric shapes of logos. By analyzing the vertices of these triangles, the system can determine the presence of specific logos in the input image. The method may also involve preprocessing the input image to enhance feature extraction, such as noise reduction or edge detection. The recognition process may further include filtering or ranking potential matches based on similarity metrics or confidence scores. This approach allows for efficient and accurate logo detection in various applications, such as brand monitoring, content moderation, or image analysis. The use of geometric shapes and their vertices simplifies the comparison process and improves recognition performance.
4. The method of claim 1 , wherein the constructing the geometric shape from the key points of the known logo and the blurred variations comprises constructing a triangle from the key points of the known logo and the blurred variations.
A method for logo recognition involves constructing geometric shapes from key points of a known logo and its blurred variations to improve detection accuracy. The method addresses challenges in identifying logos under varying visual conditions, such as blurring, distortion, or low resolution, which can obscure key features. By extracting key points from the known logo and its blurred versions, the method constructs a geometric shape, specifically a triangle, using these points. This geometric representation helps in matching and recognizing the logo even when it appears blurred or distorted in real-world images. The approach leverages the stability of geometric relationships between key points to enhance robustness against visual degradation. The method may also include preprocessing steps to enhance the logo's features before constructing the geometric shape, ensuring reliable recognition across different image qualities. This technique is particularly useful in applications like brand monitoring, automated content analysis, and visual search systems where logos need to be identified accurately under varying conditions.
5. The method of claim 1 , wherein the obtaining the unidentified logo comprises: scanning a computer network for posted images; and identifying logos within the posted images.
This invention relates to automated logo detection and identification within digital images, particularly in online environments. The problem addressed is the difficulty of efficiently locating and recognizing logos in large volumes of digital content, such as social media posts, websites, or other online platforms, where logos may appear in various formats, sizes, and contexts. The method involves scanning a computer network, such as the internet or a specific online platform, to collect posted images that may contain logos. Once images are gathered, the system analyzes them to detect and isolate logo regions within the images. This detection process may use image recognition techniques, such as pattern matching, machine learning, or computer vision algorithms, to identify logo-like structures. The system then extracts these logos for further processing, such as classification, indexing, or brand monitoring. The approach is designed to automate the discovery of logos in unstructured digital content, enabling applications like brand tracking, copyright enforcement, or market analysis. By systematically scanning networks and analyzing images, the method reduces manual effort and improves the accuracy of logo identification in large-scale digital environments.
6. The method of claim 5 , wherein the computer network comprises social media sites.
A method for analyzing and processing data within a computer network, particularly focusing on social media sites, involves collecting and evaluating user-generated content to identify patterns, trends, or anomalies. The method includes extracting data from social media platforms, such as posts, comments, or interactions, and applying analytical techniques to assess the relevance, sentiment, or engagement levels of the content. This analysis may involve natural language processing, machine learning, or statistical modeling to derive insights. The method further includes generating reports or alerts based on the analysis, which can be used for monitoring brand reputation, detecting misinformation, or improving user engagement strategies. The system may also incorporate user feedback mechanisms to refine the analysis over time. The method ensures that the data processing respects privacy regulations and user consent, while providing actionable intelligence for businesses, researchers, or policymakers. The approach is scalable to handle large volumes of social media data efficiently.
7. The method of claim 1 , the key points placed at corners and a center of a rectangle of the unidentified logo.
A system and method for identifying logos in images involves detecting key points at the corners and center of a rectangular logo region. The method first extracts a rectangular region from an image containing an unidentified logo. Key points are then placed at the four corners and the geometric center of this rectangle. These key points serve as reference locations for further analysis, such as feature extraction, matching, or recognition. The method may involve comparing these key points with known logo templates or using them to align the logo for improved recognition accuracy. The approach ensures consistent positioning of reference points, which helps in standardizing the logo's orientation and scale for reliable identification. This technique is particularly useful in automated logo detection systems where precise localization of logo regions is required before further processing. The method may be combined with additional steps such as edge detection, contour analysis, or machine learning-based classification to enhance logo recognition performance. The use of corner and center key points provides a structured way to analyze the logo's spatial characteristics, improving the robustness of the identification process.
8. The method of claim 1 , the key points placed at a highest and a lowest reach of the unidentified logo.
A system and method for identifying and analyzing logos in images or video frames involves detecting key points within a logo region to determine its orientation and position. The method places key points at the highest and lowest reaches of the unidentified logo to establish reference points for further analysis. These key points help define the logo's boundaries and facilitate alignment, scaling, or recognition processes. The system may use image processing techniques such as edge detection, contour analysis, or feature extraction to locate these key points. Once identified, the key points enable the system to compare the logo against a database of known logos, adjust for perspective distortion, or extract distinguishing features for matching. The method ensures accurate logo detection even in varying lighting conditions or partial occlusions by focusing on the logo's extreme vertical positions. This approach improves logo recognition in applications such as brand monitoring, surveillance, or automated content analysis.
9. The method of claim 1 , the key points placed at discrete disconnected portions of the unidentified logo.
A system and method for identifying and analyzing logos in images involves detecting key points at discrete, disconnected portions of an unidentified logo. The method first processes an input image to detect potential logo regions, then extracts key points from these regions. These key points are placed at specific, non-contiguous locations within the logo, ensuring they are not connected or overlapping. The key points are used to generate a structural representation of the logo, which is then compared against a database of known logos to identify matches. The system may also analyze the logo's orientation, scale, and other geometric properties based on the key points. This approach improves logo recognition accuracy by focusing on distinctive, isolated features rather than relying on continuous or connected regions. The method is particularly useful in applications such as brand monitoring, automated content analysis, and digital forensics, where precise logo identification is required. The system may further refine the key points based on additional image processing techniques, such as edge detection or feature extraction, to enhance recognition performance. The method ensures robustness against variations in image quality, lighting conditions, and logo distortions.
10. A system for image identification, the system comprising: at least one processor; and a memory storing processor-executable instructions, wherein the at least one processor is configured to implement the following operations upon executing the processor-executable instructions: creating a database of known logos, the database comprising vertices of geometric shapes formed from the known logos; wherein the creating the database of known logos comprises: obtaining a known logo; creating blurred variations of the known logo, the blurred variations comprising the known logo portrayed in varying levels of blur; identifying key points on the known logo and the blurred variations; constructing a geometric shape from the key points of the known logo and the blurred variations; calculating vertices of the geometric shape of the known logo and the blurred variations; and storing the vertices of the geometric shape of the known logo and the blurred variations in the database and associating the vertices with the known logo and the blurred variations; obtaining an unidentified logo representing an object in a query image; identifying key points on the unidentified logo; constructing a geometric shape from the key points of the unidentified logo; calculating vertices of the geometric shape of the unidentified logo; matching the vertices of the geometric shape of the unidentified logo with the vertices of the geometric shape of at least one of the known logos and the blurred variations; constructing multiple geometric shapes comprising a plurality of triangles from the key points of the known logo and the blurred variations; calculating vertices of each of the multiple geometric shapes of the known logo and the blurred variations; constructing multiple geometric shapes comprising a plurality of triangles from the key points of the unidentified logo; calculating vertices of each of the multiple geometric shapes of the unidentified logo; and matching the vertices of two or more of the geometric shapes comprising the plurality of triangles of the unidentified logo with the vertices of the geometric shapes comprising the plurality of triangles of one of the known logos and the blurred variations.
The system is designed for identifying logos in images, particularly addressing challenges in recognizing logos that may be blurred or distorted. The system creates a database of known logos by processing each logo to generate multiple blurred variations at different blur levels. For each logo and its variations, key points are identified, and geometric shapes are constructed from these points. The vertices of these shapes are calculated and stored in the database, linked to their respective logos. When an unidentified logo is encountered in a query image, the system identifies its key points, constructs geometric shapes, and calculates their vertices. The system then matches these vertices against those stored in the database to identify the logo. Additionally, the system constructs multiple geometric shapes composed of triangles from the key points of both known and unidentified logos, calculates their vertices, and performs matching between these triangular shapes to improve recognition accuracy. This approach enhances logo identification by accounting for variations in image quality and distortion.
11. The system of claim 10 , wherein the constructing the geometric shape from the key points of the unidentified logo comprises: identifying ordered pairs of the key points; identifying ordered pairs that have one key point in common; and constructing the geometric shape from the ordered pairs having one key point in common.
A system for logo recognition processes an image containing an unidentified logo by constructing a geometric shape from key points extracted from the logo. The system first identifies ordered pairs of these key points, then determines which pairs share a common key point. Using these overlapping pairs, the system constructs a geometric shape that represents the logo's structure. This geometric shape is then compared against a database of known logo shapes to identify the logo. The system may also include preprocessing steps to enhance the image quality, such as noise reduction or contrast adjustment, before key point extraction. The key points are selected based on distinctive features of the logo, such as corners, edges, or other salient points. The geometric shape construction ensures that the logo's structure is accurately represented, even if the logo is partially obscured or distorted. This method improves logo recognition accuracy by focusing on the geometric relationships between key points rather than relying solely on pixel-based matching. The system is particularly useful in applications like brand monitoring, automated content analysis, or security systems where rapid and accurate logo identification is required.
12. The system of claim 10 , wherein the database of vertices of geometric shapes formed from the known logos comprises vertices of triangles.
A system for logo recognition and analysis processes known logos by storing their geometric representations in a database. The database contains vertices of geometric shapes derived from these logos, specifically including vertices of triangles. These geometric shapes are used to compare and identify logos in images or other media. The system may extract geometric features from input images, such as edges or contours, and match them against the stored vertices to detect and recognize logos. The use of triangles as part of the geometric representation allows for efficient comparison and matching, as triangles are fundamental geometric primitives that can approximate complex logo shapes. The system may also include preprocessing steps to enhance logo detection, such as filtering or normalization, to improve accuracy. The database may be updated with new logo vertices as additional logos are added to the system. This approach enables fast and accurate logo recognition in various applications, such as branding analysis, copyright enforcement, or digital content moderation.
13. The system of claim 10 , wherein the constructing the geometric shape from the key points of the known logo and the blurred variations comprises constructing a triangle from the key points of the known logo and the blurred variations.
A system for logo recognition processes a captured image to identify a known logo, even when the logo appears blurred or distorted. The system extracts key points from the known logo and compares them to key points detected in the captured image. To account for variations in the logo's appearance, the system constructs geometric shapes, specifically triangles, using the key points from both the known logo and the blurred variations. These geometric shapes are then analyzed to determine a match between the known logo and the logo in the captured image. The system may also include a preprocessing step to enhance the captured image, such as adjusting brightness or contrast, to improve key point detection. The use of geometric shapes, particularly triangles, helps in accurately identifying the logo despite visual degradation, ensuring reliable recognition in real-world conditions. This approach is useful in applications like brand monitoring, security, or automated content analysis where logos may appear in various states of clarity.
14. The system of claim 10 , wherein the obtaining an unidentified logo comprises: scanning a computer network for posted images; and identifying logos within the posted images.
A system for detecting and analyzing logos in digital images across computer networks addresses the challenge of identifying and extracting brand-related visual content from online sources. The system scans computer networks, such as social media platforms or websites, to collect posted images. It then processes these images to detect and isolate logos present within them. The system employs image recognition techniques to identify logos, which may include brand symbols, emblems, or other distinctive visual identifiers. This functionality enables businesses and researchers to monitor brand visibility, track unauthorized use of logos, or analyze market trends based on visual data. The system may also integrate with databases of known logos to verify matches or classify detected logos by brand. By automating the detection process, the system reduces manual effort and improves the accuracy of logo identification in large-scale digital environments. The technology is particularly useful for marketing analytics, intellectual property protection, and competitive intelligence.
15. The system of claim 14 , wherein the computer network comprises social media sites.
A system for analyzing and managing data across computer networks, particularly social media platforms, addresses the challenge of efficiently processing and extracting meaningful insights from large-scale, decentralized data sources. The system includes a data collection module that gathers information from multiple social media sites, such as user posts, comments, and interactions. A processing module then filters, categorizes, and analyzes this data to identify trends, sentiment, or other relevant patterns. The system also incorporates a user interface that allows administrators to configure data collection parameters, view analysis results, and generate reports. Additionally, the system may include a security module to ensure data privacy and compliance with regulations. The integration with social media sites enables real-time monitoring of public discourse, brand reputation tracking, and targeted content delivery. By automating data extraction and analysis, the system reduces manual effort and provides actionable insights for businesses, researchers, and policymakers. The system is designed to handle high-volume, unstructured data typical of social media environments, improving decision-making and operational efficiency.
16. A method for image identification using a secure autonomous intelligent server, the method comprising: creating a data base of known logos, the data base comprising vertices of geometric shapes formed from the known logos; wherein the creating the database of known logos comprises: obtaining a known logo; creating blurred variations of the known logo, the blurred variations comprising the known logo portrayed in varying levels of blur; wherein the creating the blurred variations of the known logo comprises: receiving an original image of a known logo; applying an image sample technique to the known logo; applying an image filtering technique to the known logo to smooth image pixels and generate multiple levels of blur; and receiving multiple synthetic templates of the known logo; identifying key points on the known logo and the blurred variations; constructing a geometric shape from the key points of the known logo and the blurred variations; calculating vertices of the geometric shape of the known logo and the blurred variations; and storing the vertices of the geometric shape of the known logo and the blurred variations in the database and associating the vertices with the known logo and the blurred variations; obtaining an unidentified logo representing an object in a query image; identifying key points on the unidentified logo; constructing a geometric shape from the key points of the unidentified logo; calculating vertices of the geometric shape of the unidentified logo; matching the vertices of the geometric shape of the unidentified logo with the vertices of the geometric shape of at least one of the known logos and the blurred variations; constructing multiple geometric shapes comprising a plurality of triangles from the key points of the known logo and the blurred variations; calculating vertices of each of the multiple geometric shapes of the known logo and the blurred variations; constructing multiple geometric shapes comprising a plurality of triangles from the key points of the unidentified logo; calculating vertices of each of the multiple geometric shapes of the unidentified logo; and matching the vertices of two or more of the geometric shapes comprising the plurality of triangles of the unidentified logo with the vertices of the geometric shapes comprising the plurality of triangles of one of the known logos and the blurred variations.
This invention relates to image identification, specifically recognizing logos in query images by comparing geometric shapes derived from key points. The problem addressed is the difficulty in accurately identifying logos, especially when they appear blurred or distorted in real-world images. The solution involves creating a database of known logos, where each logo is represented by geometric shapes formed from key points. The database includes variations of each logo, such as blurred versions, to account for different visual conditions. To generate these variations, the system applies image sampling and filtering techniques to create multiple levels of blur. Key points are identified on both the original and blurred logos, and geometric shapes (e.g., triangles) are constructed from these points. The vertices of these shapes are calculated and stored in the database, linked to their respective logos. When an unidentified logo is encountered in a query image, the system identifies its key points, constructs geometric shapes, and calculates their vertices. These vertices are then matched against those in the database to identify the logo. The matching process may involve comparing multiple geometric shapes, such as triangles, to improve accuracy. This approach enhances logo recognition by accounting for variations in image quality and distortion.
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September 1, 2020
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